Methods and apparatus for colorimetrically characterizing color deviation in color imaging devices

ABSTRACT

Methods and apparatus are provided that print a set of similar color patches using a color output device, determine colorimetric values for each color patch, calculate a standard deviation value for each of the colorimetric values, and calculate a numerical value that is a function of the standard deviation values and that represents the colorimetric deviation of the set.

FIELD OF THE INVENTION

This invention relates to color imaging devices. More particularly, this invention pertains to methods and apparatus for calorimetrically characterizing color deviation in color imaging devices.

BACKGROUND

Color imaging devices, such as color copiers, color printers, color printing presses and other similar color imaging devices often exhibit visible color variations in their printed output. These visible variations may occur within a single page (spatial page variations), and also from page to page (page-to-page variations), even though the input to the imaging device is identical for each page. This phenomenon is often referred to as “engine drift,” or “engine instability,” and may result from variations in manufacturing tolerances or aging of the imaging device, time-dependent variations in the colorants used to form the image (e.g., toner or ink), and variations in the ambient environment of the device.

Although engine drift and engine instability have been recognized for some time, there has not been an objective technique to characterize this phenomenon that correlates well with the visually perceived phenomenon. Indeed, previously known techniques for characterizing engine drift have often been based on density measurements. In one such previously known technique, a color imaging device is used to print a test pattern that includes test patches, which are then measured using a densitometer or other similar device that provides measurements of reflected density. After obtaining a large number of measurements (i.e., from multiple locations within a page and from multiple pages) the measured density values are averaged, and a standard deviation measurement is calculated from the measured data.

Although such previously known density-based techniques may be used to characterize engine drift, the techniques are not useful indicators of human perception of engine drift. In particular, density measurements do not correlate well with human perception of color, and density-based indicators of color variation do not correlate well with human perception of color differences. As a result, previously known density-based techniques for characterizing engine drift do not bear a close relationship to how a human observer would perceive such engine drift.

It therefore would be desirable to provide methods and apparatus for objectively characterizing color variations in a color output device.

It further would be desirable to provide methods and apparatus for characterizing color variations in a color output device in a manner that corresponds to human perception of such variations.

SUMMARY

In view of the foregoing, it is an object of this invention to provide methods and apparatus for objectively characterizing color variations in a color output device.

It further is an object of this invention to provide methods and apparatus for characterizing color variations in a color output device in a manner that corresponds to human perception of such variations.

These and other objects of this invention are accomplished by providing methods and apparatus that print a set of similar color patches using a color output device, determine colorimetric values for each color patch, calculate a standard deviation value for each of the calorimetric values, and calculate a numerical value that is a function of the standard deviation values and that represents the colorimetric deviation of the set.

BRIEF DESCRIPTION OF THE DRAWINGS

The above-mentioned objects and features of the present invention can be more clearly understood from the following detailed description considered in conjunction with the following drawings, in which the same reference numerals denote the same elements throughout, and in which:

FIG. 1 is a block diagram of an exemplary system in accordance with this invention;

FIG. 2 is a flowchart of an exemplary method in accordance with this invention;

FIG. 3 is a diagram of an exemplary test pattern for use with systems and methods in accordance with this invention;

FIG. 4 is a diagram of exemplary output pages for use with systems and methods in accordance with this invention; and

FIG. 5 is a diagram of an alternative exemplary test pattern for use with systems and methods in accordance with this invention.

DETAILED DESCRIPTION

Referring to FIG. 1, an exemplary system for characterizing color variations in a color output device in accordance with this invention is described. System 10 includes image source 12, color imaging device 14, output pages 16, measurement device 22 and processor 24. Image source 12 includes image file 26, which includes digital data representing a test pattern 18 to be printed by color imaging device 14. Image source 12 may be a personal computer, laptop computer, handheld computer, computer workstation, print server, personal digital assistant, or any other similar device that may be used to provide image files for printing by color imaging devices.

Image source 12 may include a software application (not shown) used to generate image file 26. For example, image source 12 may be a personal computer that includes Adobe PageMaker software that may be used to generate image file 26. Image file 26 may be a digital data file that describes test pattern 18 in a page description language, such as PostScript, PCL, or other similar page description language, or may simply be a raster image, such as a TIFF image, RAW image, or other similar raster image. Color imaging device 14 may be a color printer, color copier, printing press, or other similar color imaging device that uses one or more colorants to provide output pages 16 including test pattern 18. For example, color imaging device 14 may be a color printer that uses cyan (“C”), magenta (“M”), yellow (“Y”) and black (“K”) colorants. Test pattern 18 includes one or more color patches 20.

Measurement device 22 may be any conventional measurement device that may be used to provide colorimetric data that describes a printed sample, such as a calorimeter, spectrophotometer, spectrocolorimeter, or other similar device. For example, measurement device 22 may be a Spectrolino spectrophotometer manufactured by GretagMacbeth LLC, New Windsor, N.Y. Measurement device 22 provides colorimetric data, such as CIE LAB data (referred to herein as “LAB data”) CIE XYZ data (referred to herein as “XYZ data”), CIE LUV data (referred to herein as “LUV data”), CIE LCH data (referred to herein as “LCH data”), or other similar calorimetric data that describes printed samples, such as color patches 20. Processor 24 may be a personal computer, laptop computer, handheld computer, computer workstation, print server, personal digital assistant, or any other similar device that may be used to receive colorimetric data, such as LAB data (i.e., L, a and b values), LUV data (i.e., L, u and v values), LCH data (i.e., L, C and H values), or other similar colorimetric data from measurement device 22 and generate therefrom numerical values that characterize color variations in a color output device in accordance with this invention. Persons of ordinary skill in the art will understand that the functions of processor 24 may be implemented by image source 12.

Referring now to FIGS. 1 and 2, an exemplary method 30 for characterizing color variations in a color output device in accordance with this invention is described. At step 32, color imaging device 14 is used to print test pattern 18 of one or more color patches 20 on one or more output pages 16. For example, a user of image source 12 may issue a print command to print multiple copies of image file 26 on color imaging device 14. At step 34, calorimetric values are determined for each color patch 20. For example, measurement device 22 may be used to determine LAB data for each color patch 20 on each of the output pages 16.

Referring now to FIG. 3, an exemplary output page 16 including exemplary test pattern 18 is described. Test pattern 18 includes one or more regions 40, with each region including one or more color patches 20. As shown in FIG. 3, test pattern 18 includes four regions 40 a–40 d, with each region including fifteen color patches 20. Persons of ordinary skill in the art will understand that test pattern 18 may include more or less than four regions 40, and each region may include more or less than 15 color patches 20. Each color patch 20 is comprised of a corresponding specified percentage of the colorants used by color imaging device 14 (e.g., C, M, Y and K). For example, color patches 20 aa, 20 ab, 20 ac and 20 ad may be comprised of 100% C and 0% M, 0% Y and 0% K, color patches 20 ba, 20 bb, 20 bc and 20 bd may be comprised of 0% C, 100% M, 0% Y and 100% K, and color patches 20 ca, 20 cb, 20 cc and 20 cd may be comprised of 100% C, 0% M, 100% Y and 100% K. Persons of ordinary skill in the art will understand that other percentages of colorants also may be used for color patches 20.

Each output page 16 may include multiple similar color patches 20 distributed throughout test pattern 18. For example, color patches 20 aa, 20 ab, 20 ac and 20 ad in regions 40 a–40 d are similar to one another, and are appear at four different locations throughout test pattern 18. Similarly, color patches 20 ba, 20 bb, 20 bc and 20 bd in regions 40 a–40 d are similar to one another, and are appear at four different locations throughout test pattern 18, and color patches 20 ca, 20 cb, 20 cc and 20 cd in regions 40 a–40 d are similar to one another, and are appear at four different locations throughout test pattern 18. Persons of ordinary skill in the art will understand that the specific location of each color patch 20 is not important, but similar color patches preferably should be distributed over various parts of output page 16. As described below, similar color patches 20 located within an individual output page 16 are used to determine spatial page color variations.

Referring now to FIG. 4, an exemplary set of output pages 16 are described. Each of output pages 16 ₁–16 ₅ includes test pattern 18 including color patches 20. In particular, output page 16 ₁ includes color patches 20 aa ₁ and 20 ba ₁, output page 162 includes color patches 20 aa ₂ and 20 ba ₂, output page 163 includes color patches 20 aa ₃ and 20 ba ₃, output page 164 includes color patches 20 aa ₄ and 20 ba ₄, and output page 16 ₅ includes color patches 20 aa ₅ and 20 ba ₅. Across the five output pages 16 ₁–16 ₅, color patches 20 aa ₁, 20 aa ₂, 20 aa ₃, 20 aa ₄ and 20 aa ₅ are similar to one another, and color patches 20 ba ₁, 20 ba ₂, 20 ba ₃, 20 ba ₄ and 20 ba ₅ are similar to one another. As described below, similar color patches 20 aa ₁–20 aa ₅ and 20 ba ₁–20 ba ₅ located across multiple output pages 16 ₁–16 ₅ are used to determine page-to-page color variations.

Referring again to FIGS. 1 and 2, at step 34, colorimetric values are determined for each color patch 20 on each output page 16. Thus, for example, measurement device 22 may be used to determine LAB data for each color patch 20 on each of output pages 16 ₁–16 ₅ illustrated in FIGS. 3 and 4, for a total of three hundred sets of LAB data (i.e., sixty color patches 20 on each of the five output pages 16 ₁–16 ₅). At step 36, the standard deviation of each of the L, a and b values may be determined for sets of similar color patches 20. In particular, for each output page 16, the spatial standard deviation σL_(spatial), σa_(spatial), and σb_(spatial), of each of the L, a and b values, respectively, may be determined for similar color patches 20 within the page:

$\begin{matrix} {{\sigma\; L_{spatial}} = \sqrt{\frac{\sum\limits^{\;}\;\left( {L - \overset{\_}{L}} \right)^{2}}{N - 1}}} & (1) \\ {{\sigma\; a_{spatial}} = \sqrt{\frac{\sum\limits^{\;}\;\left( {a - \overset{\_}{a}} \right)^{2}}{N - 1}}} & (2) \\ {{\sigma\; b_{spatial}} = \sqrt{\frac{\sum\;\left( {b - \overset{\_}{b}} \right)^{2}}{N - 1}}} & (3) \end{matrix}$ where L, ā and b are the average L, a and b values, respectively, in each set, and N is the number of samples per set. For example, referring again to FIG. 3, the spatial standard deviation of each of the L, a and b values may be determined for the set of color patches 20 aa–20 ad, the set of color patches 20 ba–20 bd, the set of color patches 20 ca–20 cd, and so on.

In addition, for output pages 16 ₁–16 ₅, the page-to-page standard deviation (σL_(P-P), σa_(P-P), and σb_(P-P), of each of the L, a and b values, respectively, may be determined for similar color patches 20 across the pages:

$\begin{matrix} {{\sigma\; L_{P—P}} = \sqrt{\frac{\sum\limits^{\;}\;\left( {L - \overset{\_}{L}} \right)^{2}}{N - 1}}} & (4) \\ {{\sigma\; a_{P—P}} = \sqrt{\frac{\sum\limits^{\;}\;\left( {a - \overset{\_}{a}} \right)^{2}}{N - 1}}} & (5) \\ {{\sigma\; b_{P—P}} = \sqrt{\frac{\sum\limits^{\;}\;\left( {b - \overset{\_}{b}} \right)^{2}}{N - 1}}} & (6) \end{matrix}$ where L, ā and b are the average L, a and b values, respectively, in each set, and N is the number of samples per set. For example, referring again to FIG. 4, the page-to-page standard deviation of each of the L, a and b values may be determined for the set of color patches 20 aa ₁–20 aa ₅, the set of color patches 20 ba ₁–20 ba ₅, and so on.

Referring again to FIG. 2, at step 38, a numerical value is calculated that is a function of the standard deviation values and that represents a colorimetric characterization of the color deviation of a set of similar color values. For example, a formula based on Euclidean distance calculations may be used. In particular, for each output page 16, a numerical value E_(spatial) that represents the spatial color deviation within the page may be determined for similar color patches 20 based on the corresponding standard deviation values calculated in step 36: E _(spatial)=√{square root over (σL_(spatial) ² +σa _(spatial) ² +σb _(spatial) ²)}  (7) In addition, for output pages 16 ₁–16 ₅, a numerical value E_(P-P) that represents the page-to-page color deviation across the pages may be determined for similar color patches 20 across the pages based on the corresponding standard deviation values calculated in step 36: E _(P-P)=√{square root over (σL_(P-P) ² +σa _(P-P) ² +σb _(P-P) ²)}  (8)

The following table illustrates exemplary E_(spatial) and E_(P-P) values for four color patches 20 a per output page 16, across five output pages 16 ₁–16 ₅, where each sample patch 20 a has colorant values 100% C, 0% M, 0% Y and 0% K:

Page Sample L a b 1 20aa₁ 47.53 −31.07 −55.4 1 20ab₁ 48.12 −31.09 −55.3 1 20ac₁ 44.17 −27.24 −57.09 1 20ad₁ 47.72 −31.19 −55.33 2 20aa₂ 47.72 −30.5 −54.84 2 20ab₂ 47.56 −30.58 −55.37 2 20ac₂ 43.17 −25.81 −57.34 2 20ad₂ 45.98 −29.76 −55.97 3 20aa₃ 46.95 −29.95 −55.35 3 20ab₃ 47.41 −29.8 −55.49 3 20ac₃ 43.57 −25.67 −57.11 3 20ad₃ 45.24 −28.43 −56.51 4 20aa₄ 48.21 −30.2 −55.13 4 20ab₄ 48.31 −29.73 −55.32 4 20ac₄ 44.38 −25.8 −56.86 4 20ad₄ 46.82 −29.12 −55.82 5 20aa₅ 47.19 −32.07 −55.07 5 20ab₅ 48.02 −32.27 −55.28 5 20ac₅ 43.66 −27.39 −57.24 5 20ad₅ 45.03 −29.38 −56.64

Based on these exemplary values, and using equations (1)–(3) and (7), the or deviation E_(spatial) for each page is:

Page E_(spatial) 1 2.971285 2 3.24904 3 2.735232 4 2.808842 5 3.169704 In addition, using equations (4)–(6) and (8), the page-to-page color deviation each patch location is:

Patch Location E_(P—P) a 1.00057 b 1.117555 c 0.999795 d 1.609404

Persons of ordinary skill in the art will understand that the spatial color E_(spatial) values determined across multiple pages may be averaged to provide spatial color deviation, and that the page-to-page color deviation E_(P-P) values determined across multiple patch locations may be averaged to provide an average page-to-page color deviation. Persons of ordinary skill in the art also will understand that other functions of may be used to characterize the color deviation of a set of similar color values. For example, functions based on CMC color deviation formulae or other similar color difference formulae may be used.

In addition, persons of ordinary skill in the art will understand that test pattern 18 may include more than four regions 40 a–40 d, and each region may include more than fifteen color patches 20. For example, FIG. 5 illustrates an alternative exemplary test pattern 18 that each includes four regions 40 a–40 d, with each region including sixty color patches P1–P60. Exemplary colorant values (specified in percent) for each patch are illustrated in the following table:

Patch C M Y K P1 25 0 0 0 P2 50 0 0 0 P3 75 0 0 0 P4 100 0 0 0 P5 0 25 0 25 P6 0 50 0 50 P7 0 75 0 75 P8 0 100 0 100 P9 25 25 25 0 P10 50 50 50 0 P11 75 75 75 0 P12 100 100 100 0 P13 0 0 25 25 P14 0 0 50 50 P15 0 0 75 75 P16 0 0 100 100 P17 0 25 0 0 P18 0 50 0 0 P19 0 75 0 0 P20 0 100 0 0 P21 25 25 0 25 P22 50 50 0 50 P23 75 75 0 75 P24 100 100 0 100 P25 25 0 25 25 P26 50 0 50 50 P27 75 0 75 75 P28 100 0 100 100 P29 0 0 25 0 P30 0 0 50 0 P31 0 0 75 0 P32 0 0 100 0 P33 0 0 0 25 P34 0 0 0 50 P35 0 0 0 75 P36 0 0 0 100 P37 0 25 25 0 P38 0 50 50 0 P39 0 75 75 0 P40 0 100 100 0 P41 25 0 25 0 P42 50 0 50 0 P43 75 0 75 0 P44 100 0 100 0 P45 25 25 25 25 P46 50 50 50 50 P47 75 75 75 75 P48 100 100 100 100 P49 25 0 0 25 P50 50 0 0 50 P51 75 0 0 75 P52 100 0 0 100 P53 25 25 0 0 P54 50 50 0 0 P55 75 75 0 0 P56 100 100 0 0 P57 0 25 25 25 P58 0 50 50 50 P59 0 75 75 75 P60 0 100 100 100 As previously mentioned, persons of ordinary skill in the art will understand that the specific location of each color patch 20 is not important, but similar color patches preferably should be distributed over various parts of output page 16.

The foregoing merely illustrates the principles of this invention, and various modifications can be made by persons of ordinary skill in the art without departing from the scope and spirit of this invention. 

1. A system for characterizing color variations in a color output device, the system comprising: an image file comprising a set of similar color patches, the image file adapted to be printed on the color output device; a measurement device for determining colorimetric values for each printed color patch; and a processor adapted to calculate a standard deviation value for each of the colorimetric values, and a numerical value that is a function of the standard deviation values and that represents a colorimetric characterization of color deviation of the set.
 2. The system of claim 1, wherein the color output device is a color printer.
 3. The system of claim 1, wherein the color output device is a color copier.
 4. The system of claim 1, wherein the color output device is a color printing press.
 5. The system of claim 1, wherein the color patches comprise predetermined percentages of cyan, magenta, yellow and black colorants.
 6. The system of claim 1, wherein the set of similar color patches are printed on an output page.
 7. The system of claim 1, wherein the set of similar color patches are printed on a plurality of output pages.
 8. The system of claim 1, wherein the colorimetric values comprise L, a and b values.
 9. The system of claim 1, wherein the colorimetric values comprise X, Y and Z values.
 10. The system of claim 1, wherein the measuring device comprises a colorimeter.
 11. The system of claim 1, wherein the measuring device comprises a spectrophotometer.
 12. The system of claim 1, wherein the measuring device comprises a spectrocolorimeter.
 13. The system of claim 1, wherein the function comprises a sum of squares of the standard deviation values.
 14. The system of claim 1, wherein the function comprises a square root of a sum of squares of the standard deviation values. 